Database-Augmented Query Representation for Information Retrieval
Abstract: Information retrieval models that aim to search for the relevant documents to the given query have shown many successes, which have been applied to diverse tasks. However, the query provided by the user is oftentimes very short, which challenges the retrievers to correctly fetch relevant documents. To tackle this, existing studies have proposed expanding the query with a couple of additional (user-related) features related to the query. Yet, they may be suboptimal to effectively augment the query, meanwhile, there is plenty of information available to augment it in a relational database. In this work, motivated by this, we present a novel retrieval framework called Database-Augmented Query representation (DAQu), which augments the original query with various (query-related) metadata across multiple tables. In addition, as the number of features in the metadata can be very large and there is no order among them, we encode them with our graph-based set encoding strategy, which considers hierarchies of features in the database without order. We validate DAQu in diverse retrieval scenarios that can incorporate metadata from the relational database, demonstrating that ours significantly enhances overall retrieval performance, compared to existing query augmentation methods.
Paper Type: Long
Research Area: Information Retrieval and Text Mining
Research Area Keywords: Query Expansion, Dense Retrieval
Contribution Types: NLP engineering experiment
Languages Studied: English
Submission Number: 1937
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